The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.
Single-cloud or multi-cloud data centers may be implemented using switching devices and routing devices and a software-defined network (SDN) controller that provisions the devices to support data flows between devices. The SDN controller operates as the brain of the network and is programmed to federate control among multiple SDN controller domains using common application interfaces.
In these data centers and cloud networks, traffic volume is constantly growing, resulting in an increase of encrypted data flows found in a network. Machine learning and data analysis tools may be used to classify data-flows, but maintaining throughput in real time is a challenge. The Decision Tree (DT) is a machine learning and statistics data analysis tool that can be constructed through a supervised learning process on pre-collected training data and used for analytical classification in data center and cloud environments. Additionally, DTs are used for decision analysis pertaining to data to assist in identifying a strategy most likely to reach a specific goal in a network. However, current DT implementations suffer from performance scaling issues as they have been limited to running on one host device, but the number of network devices allowed within a given network fabric is constantly growing. Furthermore, the speed and volume of data traffic flows through the network devices are also constantly growing.
Thus, there is a need for a system that allows for more efficient and accurate classification of data flows and applies the use of DT evaluations utilizing network topology and flow routing information in a cloud data center.